Robust Filtering for Markov Jump Systems by Randomized Algorithm Approach

The issue of probabilistic filtering for a class of Markov jump systems with random uncertain parameters is addressed. A scenario random approach is proposed for designing a probabilistic filter to achieve probabilistic stochastic stabilization, and satisfying an $L_{2}-L_{\infty}$ disturbance rejection performance requirement. In Particular, conditions on which the resulting filter based system is stochastically asymptotically stable with a confidence level are established.